Evacuation planning is critical for applications such as disaster management and homeland defense preparation. Efficient tools are needed to produce evacuation plans to evacuate populations to safety in the event of catastrophes, natural disasters, and terrorist attacks. Current optimal methods suffer from computational complexity and may not scale up to large transportation networks. Current naive heuristic methods do not consider the capacity constraints of the evacuation network and may not produce feasible evacuation plans. In this paper, we model capacity as a time series and use a capacity constrained heuristic routing approach to solve the evacuation planning problem. We propose two heuristic algorithms, namely Single-Route Capacity Constrained Planner and Multiple-Route Capacity Constrained Planner to incorporate capacity constraints of the routes. Experiments on a real building dataset show that our proposed algorithms can produce close-to-optimal solution, which has total evacuation time within 10 percent longer than optimal solution, and also reduce the computational cost to only half of the optimal algorithm. The experiments also show that our algorithms are scalable with respect to the number of evacuees.